Investigation of Efficient Implementation
of Local Constrained Canonical Correlation Analysis for fMRI

In previous work, local constrained canonical
correlation analysis (cCCA) methods were proposed in order to avoid model
overfitting and loss of specificity. In this work, we further investigate the
performance, efficiency and possible improvement of region-growing based cCCA
(cCCA-RG) methods. Using simulated data, we compare the estimation power of
different cCCA-RG methods as well as the exhaustive search method (cCCA-ES).
The detection power is also investigated upon real fMRI data. Our results
demonstrate that cCCA-RG can significantly improve the detection power within
an acceptable period of computation time.